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A Genome-Wide Association Study of Educational Attainment
Abstract
Twin and adoption studies have consistently found that genetic variation is an important source of heterogeneity in economic outcomes such as educational attainment and income. The advent of inexpensive, genome-wide scans is now making it increasingly feasible to directly examine specific genetic variants that predict individual differences. In this paper, we conduct a genome-wide association study (GWAS) of educational achievement. In the first stage, we used data on over 360,000 genetic markers throughout the genome from the Framingham Heart Study, a family-based sample of nearly 8,500 individuals, and found a number of markers with suggestive associations with educational attainment. The most promising variants were significant at the 5⋅10⁻⁷ level. In the second stage, we attempted to replicate the most significant first-stage associations using data from the Rotterdam study, an independent sample of over 9,500 individuals. None of the first-stage associations replicated, suggesting that the first-stage results were false positives. We discuss the challenges that arise when doing inference in genoeconomics research, emphasizing the importance of properly correcting for multiple hypothesis testing and of replicating significant results in independent samples. We also discuss issues of power and sample size. We argue that if proper attention is given to these methodological challenges, the burgeoning field of genoeconomics will add a valuable new dimension to our understanding of heterogeneity in economic outcomes.
The costs of comprehensively genotyping human subjects have fallen to the point where major funding bodies, even in the social sciences, are beginning to incorporate genetic and biological markers into major social surveys. How, if at all, should economists use and combine molecular genetic and economic data from these surveys? What challenges arise when analyzing genetically informative data? To illustrate, we present results from a "genome-wide association study" of educational attainment. We use a sample of 7,500 individuals from the Framingham Heart Study; our dataset contains over 360,000 genetic markers per person. We get some initially promising results linking genetic markers to educational attainment, but these fail to replicate in a second large sample of 9,500 people from the Rotterdam Study. Unfortunately such failure is typical in molecular genetic studies of this type, so the example is also cautionary. We discuss a number of methodological challenges that face researchers who use molecular genetics to reliably identify genetic associates of economic traits. Our overall assessment is cautiously optimistic: this new data source has potential in economics. But researchers and consumers of the genoeconomic literature should be wary of the pitfalls, most notably the difficulty of doing reliable inference when faced with multiple hypothesis problems on a scale never before encountered in social science.
The genetic and environmental contributions to educational attainment in Australia are examined using a multiple regression model drawn from the medical research literature. Data from a large sample of Australian twins are analysed. The findings indicate that at least as much as 50 percent and perhaps as much as 65 percent of the variance in educational attainments can be attributed to genetic endowments. It is suggested that only around 25 percent of the variance in educational attainments may be due to environmental factors, though this contribution is shown to be around 40 percent when adjustments for measurement error and assortative mating are made. The high fraction of the observed variation in educational attainments due to genetic differences is consistent with results reported by Heath et al. (Heath, A.C., Berg, K., Eaves, L.J., Solaas, M.H., Corey, L.A., Sundet, J., Magnus, P., Nance, W.E., 1985. Education policy and the heritability of educational attainment. Nature 314(6013), 734–736.), Tambs et al. (Tambs, K., Sundet, J.M., Magnus, P., Berg, K., 1989. Genetic and environmental contributions to the covariance between occupational status, educational attainment and IQ: a study of twins. Behavior Genetics 19(2), 209–222.), Vogler and Fulker (Vogler, G.P., Fulker, D.W., 1983. Familial resemblance for educational attainment. Behavior Genetics 13(4), 341–354.) and Behrman and Taubman (Behrman, J., Taubman, P., 1989. Is schooling mostly in the genes? Nature-nurture decomposition using data on relatives. Journal of Political Economy 97(6), 1425–1446.), suggesting that the finding is robust.
The University of California Santa Cruz Genome Browser Database (GBD) contains sequence and annotation data for the genomes
of about a dozen vertebrate species and several major model organisms. Genome annotations typically include assembly data,
sequence composition, genes and gene predictions, mRNA and expressed sequence tag evidence, comparative genomics, regulation,
expression and variation data. The database is optimized to support fast interactive performance with web tools that provide
powerful visualization and querying capabilities for mining the data. The Genome Browser displays a wide variety of annotations
at all scales from single nucleotide level up to a full chromosome. The Table Browser provides direct access to the database
tables and sequence data, enabling complex queries on genome-wide datasets. The Proteome Browser graphically displays protein
properties. The Gene Sorter allows filtering and comparison of genes by several metrics including expression data and several
gene properties. BLAT and In Silico PCR search for sequences in entire genomes in seconds. These tools are highly integrated
and provide many hyperlinks to other databases and websites. The GBD, browsing tools, downloadable data files and links to
documentation and other information can be found at http://genome.ucsc.edu/.
Oxytocin (OXT) has been implicated in a suite of complex social behaviors including observed choices in economic laboratory experiments. However, actual studies of associations between oxytocin receptor (OXTR) gene variants and experimentally elicited social preferences are rare.
We test hypotheses of associations between social preferences, as measured by behavior in two economic games, and 9 single nucleotide polymorphisms (SNPs) of the OXTR gene in a sample of Swedish twins (n = 684). Two standard economic games, the dictator game and the trust game, both involving real monetary consequences, were used to elicit such preferences. After correction for multiple hypothesis testing, we found no significant associations between any of the 9 single nucleotide polymorphisms (SNPs) and behavior in either of the games.
We were unable to replicate the most significant association reported in previous research between the amount donated in a dictator game and an OXTR genetic variant.
Prospect theory proposes the hypothesis that people have diminishing sensitivity in valuing increases in the size of monetary outcomes, for both gains and losses. For decision-making under risk, this implies a tendency to be risk-tolerant over losses while being generally risk averse over gains. We offer a neurochemistry-based model of the diminishing valuation sensitivity hypothesis. Specifically, we propose that dopamine tone modulates the sensitivity towards valuation of gains while serotonin tone modulates the sensitivity towards valuation of losses. Consequently, higher dopamine tone would yield a more concave valuation function over gains while higher serotonin tone would yield a more convex valuation function over losses. Using a neurogenetics strategy to test our neurochemical model, we find that subjects with the 9-repeat allele of DAT1 (lower DA tone) are more risk-tolerant over gains than subjects with the 10-repeat allele, and that subjects with the 10-repeat allele of STin2 (higher 5HT tone) are more risk-tolerant over losses than subjects with the 12-repeat allele. Overall, our results support the implications of our model and provide the first neurogenetics evidence that risk attitudes are partially hard-wired in differentiating between gain- and loss-oriented risks.
The current fast growth of genome-wide association studies (GWAS) combined with now common computationally expensive imputation requires the online access of large user groups to high-performance computing resources capable of analyzing rapidly and efficiently millions of genetic markers for ten thousands of individuals. Here, we present a web-based interface—called GRIMP—to run publicly available genetic software for extremely large GWAS on scalable super-computing grid infrastructures. This is of major importance for the enlargement of GWAS with the availability of whole-genome sequence data from the 1000 Genomes Project and for future whole-population efforts.
Contact: ta.knoch@taknoch.org; f.rivadeneira@erasmusmc.nl
With recent advances in understanding of the neuroscience of risk taking, attention is now turning to genetic factors that may contribute to individual heterogeneity in risk attitudes. In this paper we test for genetic associations with risk attitude measures derived from both the psychology and economics literature. To develop a long-term prospective study, we first evaluate both types of risk attitudes and find that the economic and psychological measures are poorly correlated, suggesting that different genetic factors may underlie human response to risk faced in different behavioral domains. We then examine polymorphisms in a spectrum of candidate genes that affect neurotransmitter systems influencing dopamine regulation or are thought to be associated with risk attitudes or impulsive disorders. Analysis of the genotyping data identified two single nucleotide polymorphisms (SNPs) in the gene encoding the alpha 4 nicotine receptor (CHRNA4, rs4603829 and rs4522666) that are significantly associated with harm avoidance, a risk attitude measurement drawn from the psychology literature. Novelty seeking, another risk attitude measure from the psychology literature, is associated with several COMT (catechol-O-methyl transferase) SNPs while economic risk attitude measures are associated with several VMAT2 (vesicular monoamine transporter) SNPs, but the significance of these associations did not withstand statistical adjustment for multiple testing and requires larger cohorts. These exploratory results provide a starting point for understanding the genetic basis of risk attitudes by considering the range of methods available for measuring risk attitudes and by searching beyond the traditional direct focus on dopamine and serotonin receptor and transporter genes.
We use unique Swedish data with information on adopted children's biological and adoptive parents to estimate intergenerational mobility associations in earnings and education. We argue that the impact from biological parents captures broad prebirth factors, including genes and prenatal environment, and the impact from adoptive parents represents broad postbirth factors, such as childhood environment. We find that both pre- and postbirth factors contribute to intergenerational earnings and education transmissions, and that prebirth factors are more important for mother's education and less important for father's income. We also find some evidence for a positive interaction effect between postbirth environment and prebirth factors. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
Individuals vary in their willingness to take financial risks. Here we show that variants of two genes that regulate dopamine and serotonin neurotransmission and have been previously linked to emotional behavior, anxiety and addiction (5-HTTLPR and DRD4) are significant determinants of risk taking in investment decisions. We find that the 5-HTTLPR s/s allele carriers take 28% less risk than those carrying the s/l or l/l alleles of the gene. DRD4 7-repeat allele carriers take 25% more risk than individuals without the 7-repeat allele. These findings contribute to the emerging literature on the genetic determinants of economic behavior.
In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data retrieval systems and computational resources for the analysis of data in GenBank and other biological data made available through NCBI's website. NCBI resources include Entrez, Entrez Programming Utilities, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD) and the Conserved Domain Architecture Retrieval Tool (CDART). Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized datasets. All of the resources can be accessed through the NCBI home page at http://www.ncbi.nlm.nih.gov.
Population stratification occurs in case-control association studies when allele frequencies differ between cases and controls because of ancestry. Stratification may lead to false positive associations, although this issue remains controversial. Empirical studies have found little evidence of stratification in European-derived populations, but potentially significant levels of stratification could not be ruled out. We studied a European American panel discordant for height, a heritable trait that varies widely across Europe. Genotyping 178 SNPs and applying standard analytical methods yielded no evidence of stratification. But a SNP in the gene LCT that varies widely in frequency across Europe was strongly associated with height (P < 10(-6)). This apparent association was largely or completely due to stratification; rematching individuals on the basis of European ancestry greatly reduced the apparent association, and no association was observed in Polish or Scandinavian individuals. The failure of standard methods to detect this stratification indicates that new methods may be required.
Although bupropion and nicotine replacement therapy (NRT) are efficacious tobacco dependence treatments, there is substantial interindividual variability in therapeutic response and most smokers relapse. Pharmacogenetics research may improve treatment outcomes by identifying genetic variants predictive of therapeutic response. We investigated the roles of two functional genetic variants in the dopamine D2 receptor (DRD2) gene in response to pharmacotherapy for tobacco dependence among participants in two randomized clinical trials with a 6-month follow-up period: a double-blind placebo-controlled trial of bupropion (n=414) and an open label trial of transdermal nicotine vs nicotine nasal spray (n=368). At the end of the treatment phase, a statistically significant (p=0.01) interaction between the DRD2 - 141C Ins/Del genotype and treatment indicated a more favorable response to bupropion among smokers homozygous for the Ins C allele compared to those carrying a Del C allele. By contrast, smokers carrying the Del C allele had statistically significantly (p=0.006) higher quit rates on NRT compared to those homozygous for the Ins C allele, independent of NRT type. The C957T variant was also associated (p=0.03) with abstinence following NRT. These results suggest that bupropion may be the preferred pharmacologic treatment for smokers homozygous for the DRD2 - 141 Ins C allele, while NRT may be more beneficial for those who carry the Del C allele. Study findings require confirmation in additional larger samples before they are applied in practice.
The objectives of our study were to evaluate whether genetic variation in nicotine metabolic inactivation accounted for the emergence of nicotine dependence from mid- to late adolescence and whether initial smoking experiences mediated this effect.
Participants were 222 adolescents of European ancestry who participated in a longitudinal cohort study of the biobehavioral determinants of adolescent smoking. Survey data were collected annually from grade 9 to the end of grade 12. Self-report measures included nicotine dependence, smoking, age first smoked, initial smoking experiences, peer and household member smoking, and alcohol and marijuana use. DNA collected via buccal swabs was assessed for CYP2A6 alleles that are common in white people and are demonstrated to decrease enzymatic function (CYP2A6*2, *4, *9, *12).
Latent growth-curve modeling indicated that normal metabolizers (individuals with no detected CYP2A6 variants) progressed in nicotine dependence at a faster rate and that these increases in nicotine dependence leveled off more slowly compared with slower metabolizers (individuals with CYP2A6 variants). Initial smoking experiences did not account for how CYP2A6 genetic variation impacts nicotine dependence.
These findings may help to promote a better understanding of the biology of smoking behavior and the emergence of nicotine dependence in adolescents and inform future work aimed at understanding the complex interplay between genetic, social, and psychological factors in adolescent smoking behavior.
The Genetic Association Information Network (GAIN) is a public-private partnership established to investigate the genetic basis of common diseases through a series of collaborative genome-wide association studies. GAIN has used new approaches for project selection, data deposition and distribution, collaborative analysis, publication and protection from premature intellectual property claims. These demonstrate a new commitment to shared scientific knowledge that should facilitate rapid advances in understanding the genetics of complex diseases.
Author Summary
Genetic variation in quantitative or complex traits can be partitioned into many components due to additive, dominance, and interaction effects of genes. The most important is the additive genetic variance because it determines most of the correlation of relatives and the opportunities for genetic change by natural or artificial selection. From reviews of the literature and presentation of a summary analysis of human twin data, we show that a high proportion, typically over half, of the total genetic variance is additive. This is surprising as there are many potential interactions of gene effects within and between loci, some revealed in recent QTL analyses. We demonstrate that under the standard model of neutral mutation, which leads to a U-shaped distribution of gene frequencies with most near 0 or 1, a high proportion of additive variance would be expected regardless of the amount of dominance or epistasis at the individual loci. We also show that the model is compatible with observations in populations undergoing selection and results of QTL analyses on F2 populations.
Individuals exhibit substantial heterogeneity in financial risk aversion. Recent work on twins demonstrated that some variation is influenced by individual heritable differences. Despite this, there has been no study investigating possible genetic loci associated with financial risk taking in healthy individuals. Here, we examined whether there is an association between financial risk preferences, elicited experimentally in a game with real monetary payoffs, and the presence of the 7-repeat allele (7R+) in the dopamine receptor D4 gene as well as the presence of the A1 allele (A1+) in the dopamine receptor D2 gene in 94 young men. Although we found no association between the A1 allele and risk preferences, we did find that 7R+ men are significantly more risk loving than 7R− men. This polymorphism accounts for roughly 20% of the heritable variation in financial risk taking. We suggest that selection for the 7R allele may be for a behavioral phenotype associated with risk taking. This is consistent with previous evolutionary explanations suggesting that selection for this allele was for behaviors associated with migration and male competition, both of which entail an element of risk.
This paper examines the influence of health conditions on academic performance during adolescence. To account for the endogeneity of health outcomes and their interactions with risky behaviors we exploit natural variation within a set of genetic markers across individuals. We present evidence that specific genetic markers have good statistical properties to identify the impacts of ADHD, depression and obesity. These markers help reveal a new dynamism from poor health to lower academic achievement with substantial heterogeneity in their impacts across genders. Our investigation further exposes the considerable challenges in identifying health impacts due to the prevalence of comorbid health conditions, with clear implications for the health economics literature.
The authors modify R. A. Fisher's model by incorporating measures on the environment that may be correlated across kin groups. They estimate a model of schooling attainment using data on eight kin groups and find a large contribution of generic endowments to the variance in schooling, though certain aspects of the environment matter. Copyright 1989 by University of Chicago Press.
Time and value are related concepts that influence human behaviour. Although classical topics in human thinking throughout the ages, few environmental economic non-market valuation studies have attempted to link the two concepts. Economists have estimated non-market environmental values in monetary terms for over 30 years. This history of valuation provides an opportunity to compare value estimates and how valuation techniques have changed over time. This research aims to compare value estimates of benefits of a protected natural area. In 1978, Nadgee Nature Reserve on the far south coast of New South Wales was the focus of the first application of the contingent valuation method in Australia. This research aims to replicate that study using both the original 1978 contingent valuation method questionnaire and sampling technique, as well as state of the art non-market valuation tools. This replication will provide insights into the extent and direction of changes in environmental values over time. It will also highlight the impact on value estimates of methodological evolution. These insights will help make allocating resources more efficient.
This paper reviews a number of recent studies of the income schooling-ability nexus using sibling data and discusses the problem of identification in such studies. Special emphasis is placed on the role of errors in variables, concluding that modest error levels can account for much of the observed difference between total and within-family estimates of returns to schooling. It also suggests that the family may not contribute as much to the transmission of inequality as is commonly thought, since it is a force for equality within (among siblings).
A major and well-recognized difficulty in estimating the effects of education on earnings is that the more educated are likely to be more able, irrespective of education. If ability also determines earnings and is not controlled, ordinary least squares will yield biased estimates of the education coefficient. In this study, we use data on identical twins to control for differences in ability that arise from genetic endowments and family environment. Not controlling for genetics and family environment may cause a large bias, up to two-thirds of the noncontrolled coefficient.
Given suitable data, we can estimate the degree to which genetic variation explains observed variation in human behavior (h^2). But, contrary to what most investigators assume, the amount of nongenetic variation (1-h^2) sets no upper limit on the explanatory power of environmental variation. This is because genetic variation often causes environmental variation, so the two categories are not mutually exclusive. The assumption that the two categories are mutually exclusive derives from a narrowly physiological model of the way in which genes affect behavior. This model is inconsistent with all the most familiar examples of genes affecting behavior, especially the examples of race and sex. Heritability estimates set no upper limit on the potential effect of reducing or eliminating variation in environmental factors that currently vary in response to genotype, as many do. Nor do they set an upper limit on the effect of creating new environments. Heritability estimates do set an upper limit on the effect of reducing or eliminating environmental variations that are independent of genotype, but other statistics usually provide even better estimates of these effects. There is no evidence that genetically based inequalities are harder to eliminate than other inequalities. Until we know how genes affect specific forms of behavior, heritability estimates will tell us almost nothing of importance.
Medical researchers are rapidly identifying the genetic causes of many diseases. Genes that increase the risk of contracting Alzheimer's, colon and breast cancer, Huntington's, cystic fibrosis and numerous other diseases have been identified. Genetic tests can reveal an individual's probable health status many years in advance of sickness. Many fear that this will lead to 'genetic discrimination' in the employment and health insurance markets. Solutions such as consent laws are impractical and create adverse selection problems. A new form of insurance, genetic insurance, can eliminate these problems and allow everyone to be insured.
The pathways involved in neuronal survival or death have been extensively studied mainly in cell lines. Recent evidence has suggested that activation of the stress activated pathways, jun N-terminal kinase (JNK) and p38 may play important roles in neuronal cell death or regeneration. In this review we will discuss these pahtways in detail. We will examine the evidence that these pathways are important in neuronal cell death. Finally we will review the evidence that inhibitors of these pathways have a neuroprotective effect both in vitro and in vivo.
Investigators conducting studies of the molecular genetics of complex traits in humans often need rationally to select a set of single nucleotide polymorphisms (SNPs) from the hundreds or thousands available for a candidate gene. Accomplishing this requires integration of genomic data from distributed databases and is both time-consuming and error-prone. We developed the TAMAL (Technology And Money Are Limiting) web site to help identify promising SNPs for further investigation. For a given list of genes, TAMAL identifies SNPs that meet user-specified criteria (e.g. haplotype tagging SNPs or SNP predicted to lead to amino acid changes) from current versions of online resources (i.e. HapMap, Perlegen, Affymetrix, dbSNP and the UCSC genome browser).
Availability: TAMAL is a platform independent web-based application available free of charge at Author Webpage
Contact: pfsulliv@med.unc.edu
Supplementary information: Author Webpage
Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
Genome-wide association studies (GWAS) provide an important approach to identifying common genetic variants that predispose to human disease. A typical GWAS may genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) located throughout the human genome in a set of cases and controls. Logistic regression is often used to test for association between a SNP genotype and case versus control status, with corresponding odds ratios (ORs) typically reported only for those SNPs meeting selection criteria. However, when these estimates are based on the original data used to detect the variant, the results are affected by a selection bias sometimes referred to the "winner's curse" (Capen and others, 1971). The actual genetic association is typically overestimated. We show that such selection bias may be severe in the sense that the conditional expectation of the standard OR estimator may be quite far away from the underlying parameter. Also standard confidence intervals (CIs) may have far from the desired coverage rate for the selected ORs. We propose and evaluate 3 bias-reduced estimators, and also corresponding weighted estimators that combine corrected and uncorrected estimators, to reduce selection bias. Their corresponding CIs are also proposed. We study the performance of these estimators using simulated data sets and show that they reduce the bias and give CI coverage close to the desired level under various scenarios, even for associations having only small statistical power.
Economists have argued that obesity may lead to worse labor market outcomes, especially for women. Empirical methods to test this hypothesis have not thus far adequately controlled for the endogeneity of obesity. We use variation in genotype to predict variation in phenotype (obesity). Genetic information from specific genes linked to obesity in the biomedical literature provides strong exogenous variation in the body mass index and thus can be used as instrumental variables. These genes predict swings in weight of between 5 and 20 pounds for persons between five and six feet tall. We use additional genetic information to control for omitted variables correlated with both obesity and labor market outcomes. We analyzed data from the third wave of the Add Health data set, when respondents are in their mid-twenties. Results from our preferred models show no effect of lagged obesity on the probability of employment or on wages, for either men or women. This paper shows the potential of using genetic information in social sciences.
Genes and Social Strati…cation Amsterdam: North-HollandIndividual Di¤erences in Allocation of Funds in the Dictator Game Associated with Length of the Arginine Vasopressin 1a Receptor (AVPR1a) RS3 Promoter Region and Correlation between RS3 Length and Hippocampal mRNA
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Analyzing Genome-Wide Associa-tion Study Data: A Tutorial Using PLINK
- Patrick F Sullivan
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Sullivan, Patrick F., and Shaun Purcell. 2008. “Analyzing Genome-Wide Associa-tion Study Data: A Tutorial Using PLINK.” In Statistical Genetics; Gene Mapping Through Linkage and Association, eds. Benjamin M. Neale, Manuel A.R. Ferreira, Sarah Medland and Danielle Posthuma, 355–394. New York: Taylor & Francis Group
Beware the Chopstick Gene
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Paul Licht-enstein, and Björn WallaceGenetic Variation in Preferences for Giving and Risk-Taking
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Cesarini, David, Christopher T. Dawes, Magnus Johannesson, Paul Licht-enstein, and Björn Wallace. 2009. “Genetic Variation in Preferences for Giving and Risk-Taking.” Quarterly Journal of Economics, 124(2): 809–841
Culture and Inequality
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Feldman, Marcus W, Sarah P. Otto, and Freddy B. Christiansen. 2000.“Genes, Culture and Inequality.” In Meritocracy and Economic Inequality, eds. Kenneth Arrow, Samuel Bowles and Steven Durlauf, 61–85. Princeton: Princeton: Princeton University Press
Huntington’s Disease
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Mach 1.0: Rapid Haplotype Reconstruc-tion and Missing Genotype Inference
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Li, Yun, and Gonçalo R. Abecasis. 2006. “Mach 1.0: Rapid Haplotype Reconstruc-tion and Missing Genotype Inference.” American Journal of Human Genetics S79: 2290
A Haplotype Map of the Human GenomeEmpirical Strategies in Labor Economics
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